Propelling data-driven innovation to accelerate energy transition

A world leader in energy solutions and technology with a global presence intends to accelerate its data-driven innovation. The company wanted to unleash the potential of its enterprise data assets to accelerate the global transition to sustainable energy solutions and set out to develop a cutting-edge platform for enterprise data and advanced analytics that breaks data siloes, integrates and streamlines access to all data sources, and facilitates faster development of innovative solutions. An analysis by Accenture, in collaboration with the World Economic Forum, states that “digital technologies, if scaled across industries, can deliver up to 20 percent of the 2050 reduction needed to hit the International Energy Agency net-zero trajectories in the energy, materials, and mobility industries.”

The company wanted the platform to help instill a data-driven culture across the company and facilitate the digital DNA of all its products and services. To verify that the platform was designed and built to achieve these goals, the company used Amazon Web Services (AWS) to envision and deliver the platform and its capabilities. The project mandate was focused on delivering quantifiable business value and providing a delightful user experience. To accomplish these goals, the company used the AWS “Working Backwards” process, starting with the goals and working backward. This method engaged all the stakeholders in articulating the platform vision and verified that the technical implementation responded to clear business and user needs.

Using the “Working Backwards” process

The advanced data and analytics platform was an ambitious initiative that would impact many stakeholders across the company, and it required extensive collaboration and agreement to realize its goals. Therefore, it was imperative to start the journey with a clearly articulated vision for all stakeholders to rally behind. The platform team used the Working Backwards process to articulate the vision and establish what success would look like.

The Working Backwards process started with an executive visioning workshop, where all relevant stakeholders were invited to share their proposed press releases for the platform’s future launch. This exercise highlighted a significant overlap in what the different stakeholders wanted to see the platform achieve, which helped in building consensus and establishing common ground for collaboration. The areas in which the visions diverged also enriched the deliberations and helped the team to formulate a richer vision that captured the wide range of possibilities for the platform. Once the platform vision was formulated, the stakeholders collectively identified personas for the platform’s potential users and defined the platform’s key value proposition to each persona.

The stakeholders also defined the key success metrics that would demonstrate that the platform had successfully delivered its intended outcomes. The defined success metrics included the total financial value of business outcomes realized through platform use, a reduction in time to market for new advanced analytics use cases, and the level of platform adoption across the company.

The executive visioning exercise was followed by a series of focused workshops with representatives for each persona to map out the persona’s user journey on the platform and to define the features the platform should deliver to facilitate this journey. The features were captured in the form of epics to facilitate feature refinement, breakdown into smaller user stories, and implementation.

Since the platform was intended to facilitate rapid innovation on many data-driven use cases, it was expected to deliver capabilities that support these anticipated use cases and to continually evolve to satisfy the requirements of future use cases. The platform also required features to support its effective operations and ongoing maintenance. Consequently, the platform’s feature backlog was structured to collect input from three primary sources: use case features, user journey features, and platform operational requirements.

Establishing effective ways of working
The project team had to establish effective, agile, and adaptable ways of working to build and deliver an ambitious, strategic, and rapidly evolving platform. These new ways of working were key to managing the large number of desired features in order to optimize the delivery of business value, verify continual progress in feature implementation and release, and remain agile and responsive to feedback and changes in requirements.

Prioritizing features and planning delivery

To design the platform, the project team used the Scaled Agile Framework (SAFe). The team started by establishing a prioritization mechanism using SAFe’s Weighted Shortest Job First (WSJF) methodology and worked with all relevant stakeholders to estimate the business value for each feature. The team then enlisted the technical experts to estimate the job size for each feature and come to a ranked priority across all defined features.

This feature prioritization exercise provided many benefits. First, it clarified the feature delivery sequence. Second, it demonstrated commitment to deliver value to stakeholders by prioritizing features based on the input of the persona representatives. And third, it focused the team’s energy on refining and detailing the prioritized features. By focusing on a smaller number of high-priority, high-value features, the team was able to accelerate development and delivery and avoid spending energy on low-priority features that may change or be completely removed, depending on user feedback and usage data.

Once the stakeholders agreed on feature priorities, the established ways of working facilitated efficient release planning for the first release of the platform, or the minimum viable product (MVP). The project team selected the highest-priority features and planned the next program increment (PI) over six 2-week sprints. The team used agile project management tooling to document and track work and to facilitate collaboration between team members. The robust ways of working, powered by effective tooling, helped the team to complete and deliver the first features after only two sprints.

Assessing learning and iterating to optimize value delivery

From the outset, the project team set out to intentionally build learning and adaptation mechanisms into their ways of working. This approach meant that the team did not define long-term, deterministic release plans and instead used short, iterative planning cycles. The team set expectations with the stakeholders that the vision crafted through the Working Backwards process would capture their aspirations at that specific point in time, and this vision would be regularly refreshed to reflect changing market and technology dynamics, lessons learned from released features, and user feedback.

While it is still early to demonstrate the full impact of the platform development, early outcomes show clear delivery of value that can be attributed to the proper and inclusive prioritization of platform features and fast feature releases through the agile ways of working. For example, the MVP helped business users to develop several solutions to pressing use cases in just a few weeks, a process that would have had to wait several months if the platform development had followed a traditional delivery approach.

Conclusion

Envisioning, planning, and delivering an ambitious analytics platform that is aimed to drive data-driven innovation is a significant undertaking. In this case, several key factors helped the team to imagine, define, and deliver a robust platform that will remain adaptive and responsive to the company’s current and future needs. First, early consensus on the platform vision, its key value proposition to each user persona, and its success metrics was key to setting expectations and mobilizing participation. Second, establishing effective ways of working with clear value-driven prioritization and agile, collaborative release planning facilitated the fast release of high-value features. This fast release helped users to innovate and create data-driven solutions to pressing business and operational use cases. Third, a built-in, intentional approach to learning established open channels to collect adoption metrics and user feedback. These insights are used to continually improve the platform’s vision and future iterations to remain relevant and as cutting edge as the innovations it aims to empower.